This software Presented by “Bruna Moreira da Silva”, Epitope3D is an epitope prediction tool using graph-based signatures, developed at the “University of Melbourne“, which is an example of a structure-based approach to epitope prediction. It was developed and trained using a curated, non-redundant set of 200 antigen structures with marked epitopes.
Individual residues, labeled according to whether they belong to the epitope, are characterized using graph-based signatures of the neighborhood of each residue, representing geometry and chemical composition of the environment, making it a similar approach to recent attempts to utilize geometric features in protein model assessment and property prediction.
The resulting signatures are used as input to a Adaboost classifier, which is tested against a set of 45 held-out antigens. In a comparison study against several other epitope prediction tools, Epitope3D boosted impressive classification performance. It will be interesting to see further evaluation of this tool.